How data collection and Machine Learning optimises your getback treatment

By Adam Cabble, getback Head of Exercise Physiology

In the allied health world, if providers don’t collect data, it’s a challenge to measure treatment outcomes. And for those who collect data, the challenge is trying to understand how to best use it.

getback™ has data. In fact, we start collecting data the moment a patient walks in the door, and continue collecting data until their treatment journey ends. 

Initial consultation and questionnaire

Data collection starts with the patient being assigned a unique identifier in our system with all identifying details removed.

Patients are able to track their improvement in their data with our Physiotherapists.
getback data collection begins with an interview and questionnaire about your specific back pain.

During an initial physiotherapy consultation, patient data is captured digitally based on an interview and online questionnaire, including:

  • Site of pain
  • Duration of pain since symptoms began
  • Frequency of symptoms
  • Severity of symptoms
  • Pattern of pain – which movements make the pain worse, movements that don’t affect it and movements that may reduce pain.

Mobility and strength assessment

Once any pain has stabilised, patients complete a mobility and strength assessment on the getback rehabilitation devices.

The mobility and strength assessment builds a detailed profile of your spinal muscle strength, range of motion and imbalances.

getback devices enable the range of motion and strength of your spinal muscles to be precisely measured. The getback system then builds a data profile of your spinal muscle strengths, weaknesses and imbalances.

From this starting point, a total of 283 data points are collected for each patient throughout the course of their treatment program.

What does getback do with the data?

Our extensive data collections gives us the opportunity to analyse patient outcomes using Machine Learning (ML). 

For example: Which patients had a great outcome and why? Why did some patients take longer to improve than others?

Machine Learning and identifying patterns

One of the most significant benefits for getback using ML is the ability to analyse vast amounts of data and identify intricate patterns. 

By leveraging data about back pain conditions such as what relieves symptoms and strength and range of motion measurements, ML algorithms can develop a comprehensive understanding of patterns and how patients improve. This analysis of our data feeds back into our treatment programs.

Refining your individual back pain treatment program

A key pillar of the getback treatment process is ‘progressive overload’ (making each exercise session slightly harder). Your therapist makes changes to your program every visit in a variety of ways, including:

Data collected by the getback devices and Machine Leaning help your therapist make progressive adjustments to your program.
  • Increasing weight
  • Increasing repetitions
  • Increasing range of motion (making you push the weight further)
  • Doing the exercise faster or slower 

For the therapist, getting the right form of progression is partly understanding your needs, and partly understanding the treatment. Machine Learning adds a layer of knowledge for your therapist about what has worked for similar patients with similar pain presentations. 

While having Machine Leaning data feed into our treatment programs leads to improved patient outcomes, it’s important to understand that ML does not replace the therapist’s clinical skill. ML is a tool that improves the clinical decision making process. 

The therapist will always control your treatment and supervise your getback sessions – they will just have the analysed data and knowledge from thousands of patients to help them to help you!

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